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Record W4416048217 · doi:10.1093/police/paaf037

Exploring gender differences in policing: the role of workplace social support on the mental health and wellbeing of parents in policing

2025· article· en· W4416048217 on OpenAlexfundno aff
Mahnoz Illias, Kathleen Riach, Ioannis Basinas, Jessica K. Miller, Brendan Burchell, Evangelia Demou

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
FundersMedical Research Council CanadaChief Scientist Office
KeywordsMental healthPsychological interventionSocial supportOddsLogistic regressionMultilevel modelBalance (ability)Odds ratioOccupational safety and health

Abstract

fetched live from OpenAlex

Abstract Police officers are more likely to suffer from mental health conditions compared with other first responders. Women in policing face disproportionately higher risks of anxiety, depression, and sleep disturbances. Workplace social support (WSS) can mitigate these effects, but its interaction with gender and parenthood remains understudied. This study investigates gender differences in the relationship between WSS, mental health, and overall well-being outcomes among police professionals and examines how parenthood moderates these associations. We conducted a secondary analysis of The Job & The Life survey using hierarchical logistic regression to assess anxiety, depression, overall wellbeing, work-life balance and sleep disturbances across WSS levels. Poor WSS was linked to worse outcomes for both genders. Mothers had higher odds of anxiety, depression, and sleep disturbances but reported better work-life balance than fathers. WSS plays a critical role in mitigating adverse outcomes, yet mothers remain vulnerable despite good WSS. This calls for targeted organizational interventions for women and parents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.409
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.178
GPT teacher head0.431
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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